Kea show three signatures of domain-general statistical inferenceBastos, Amalia P. M.; Taylor, Alex H.
doi: 10.1038/s41467-020-14695-1pmid: 32127523
One key aspect of domain-general thought is the ability to integrate information across different cognitive domains. Here, we tested whether kea (Nestor notabilis) can use relative quantities when predicting sampling outcomes, and then integrate both physical information about the presence of a barrier, and social information about the biased sampling of an experimenter, into their predictions. Our results show that kea exhibit three signatures of statistical inference, and therefore can integrate knowledge across different cognitive domains to flexibly adjust their predictions of sampling events. This result provides evidence that true statistical inference is found outside of the great apes, and that aspects of domain-general thinking can convergently evolve in brains with a highly different structure from primates. This has important implications not only for our understanding of how intelligence evolves, but also for research focused on how to create artificial domain-general thought processes.
Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networksBazgir, Omid; Zhang, Ruibo; Dhruba, Saugato Rahman; Rahman, Raziur; Ghosh, Souparno; Pal, Ranadip
doi: 10.1038/s41467-020-18197-ypmid: 32873806
Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to arrange high-dimensional vectors in a compact image form conducible for CNN-based deep learning. We consider the similarities between features to generate a concise feature map in the form of a two-dimensional image by minimizing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach. We hypothesize that this approach enables embedded feature extraction and, integrated with CNN-based deep learning, can boost the predictive accuracy. We illustrate the superior predictive capabilities of the proposed framework as compared to state-of-the-art methodologies in drug sensitivity prediction scenarios using synthetic datasets, drug chemical descriptors as predictors from NCI60, and both transcriptomic information and drug descriptors as predictors from GDSC.
Fiber reinforced GelMA hydrogel to induce the regeneration of corneal stromaKong, Bin; Chen, Yun; Liu, Rui; Liu, Xi; Liu, Changyong; Shao, Zengwu; Xiong, Liming; Liu, Xianning; Sun, Wei; Mi, Shengli
doi: 10.1038/s41467-020-14887-9pmid: 32188843
Regeneration of corneal stroma has always been a challenge due to its sophisticated structure and keratocyte-fibroblast transformation. In this study, we fabricate grid poly (ε-caprolactone)-poly (ethylene glycol) microfibrous scaffold and infuse the scaffold with gelatin methacrylate (GelMA) hydrogel to obtain a 3 D fiber hydrogel construct; the fiber spacing is adjusted to fabricate optimal construct that simulates the stromal structure with properties most similar to the native cornea. The topological structure (3 D fiber hydrogel, 3 D GelMA hydrogel, and 2 D culture dish) and chemical factors (serum, ascorbic acid, insulin, and β-FGF) are examined to study their effects on the differentiation of limbal stromal stem cells to keratocytes or fibroblasts and the phenotype maintenance, in vitro and in vivo tissue regeneration. The results demonstrate that fiber hydrogel and serum-free media synergize to provide an optimal environment for the maintenance of keratocyte phenotype and the regeneration of damaged corneal stroma.
Nanoscopic diffusion of water on a topological insulatorTamtögl, Anton; Sacchi, Marco; Avidor, Nadav; Calvo-Almazán, Irene; Townsend, Peter S. M.; Bremholm, Martin; Hofmann, Philip; Ellis, John; Allison, William
doi: 10.1038/s41467-019-14064-7pmid: 31937778
The microscopic motion of water is a central question, but gaining experimental information about the interfacial dynamics of water in fields such as catalysis, biophysics and nanotribology is challenging due to its ultrafast motion, and the complex interplay of inter-molecular and molecule-surface interactions. Here we present an experimental and computational study of the nanoscale-nanosecond motion of water at the surface of a topological insulator (TI), Bi2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${}_{2}$$\end{document}Te3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${}_{3}$$\end{document}. Understanding the chemistry and motion of molecules on TI surfaces, while considered a key to design and manufacturing for future applications, has hitherto been hardly addressed experimentally. By combining helium spin-echo spectroscopy and density functional theory calculations, we are able to obtain a general insight into the diffusion of water on Bi2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${}_{2}$$\end{document}Te3\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${}_{3}$$\end{document}. Instead of Brownian motion, we find an activated jump diffusion mechanism. Signatures of correlated motion suggest unusual repulsive interactions between the water molecules. From the lineshape broadening we determine the diffusion coefficient, the diffusion energy and the pre-exponential factor.
One-pot biocatalytic route from cycloalkanes to α,ω‐dicarboxylic acids by designed Escherichia coli consortiaWang, Fei; Zhao, Jing; Li, Qian; Yang, Jun; Li, Renjie; Min, Jian; Yu, Xiaojuan; Zheng, Gao-Wei; Yu, Hui-Lei; Zhai, Chao; Acevedo-Rocha, Carlos G.; Ma, Lixin; Li, Aitao
doi: 10.1038/s41467-020-18833-7pmid: 33028823
Aliphatic α,ω‐dicarboxylic acids (DCAs) are a class of useful chemicals that are currently produced by energy-intensive, multistage chemical oxidations that are hazardous to the environment. Therefore, the development of environmentally friendly, safe, neutral routes to DCAs is important. We report an in vivo artificially designed biocatalytic cascade process for biotransformation of cycloalkanes to DCAs. To reduce protein expression burden and redox constraints caused by multi-enzyme expression in a single microbe, the biocatalytic pathway is divided into three basic Escherichia coli cell modules. The modules possess either redox-neutral or redox-regeneration systems and are combined to form E. coli consortia for use in biotransformations. The designed consortia of E. coli containing the modules efficiently convert cycloalkanes or cycloalkanols to DCAs without addition of exogenous coenzymes. Thus, this developed biocatalytic process provides a promising alternative to the current industrial process for manufacturing DCAs.
Publisher Correction: Unveiling the Re effect in Ni-based single crystal superalloysWu, Xiaoxiang; Makineni, Surendra Kumar; Liebscher, Christian H.; Dehm, Gerhard; Mianroodi, Jaber Rezaei; Shanthraj, Pratheek; Svendsen, Bob; Bürger, David; Eggeler, Gunther; Raabe, Dierk; Gault, Baptiste
doi: 10.1038/s41467-020-14820-0pmid: 32081900
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
D-serine mitigates cell loss associated with temporal lobe epilepsyBeesley, Stephen; Sullenberger, Thomas; Crotty, Kathryn; Ailani, Roshan; D’Orio, Cameron; Evans, Kimberly; Ogunkunle, Emmanuel O.; Roper, Michael G.; Kumar, Sanjay S.
doi: 10.1038/s41467-020-18757-2pmid: 33009404
Temporal lobe epilepsy (TLE) is the most common type of drug-resistant epilepsy in adults, with an unknown etiology. A hallmark of TLE is the characteristic loss of layer 3 neurons in the medial entorhinal area (MEA) that underlies seizure development. One approach to intervention is preventing loss of these neurons through better understanding of underlying pathophysiological mechanisms. Here, we show that both neurons and glia together give rise to the pathology that is mitigated by the amino acid D-serine whose levels are potentially diminished under epileptic conditions. Focal administration of D-serine to the MEA attenuates neuronal loss in this region thereby preventing epileptogenesis in an animal model of TLE. Additionally, treatment with D-serine reduces astrocyte counts in the MEA, alters their reactive status, and attenuates proliferation and/or infiltration of microglia to the region thereby curtailing the deleterious consequences of neuroinflammation. Given the paucity of compounds that reduce hyperexcitability and neuron loss, have anti-inflammatory properties, and are well tolerated by the brain, D-serine, an endogenous amino acid, offers new hope as a therapeutic agent for refractory TLE.
Reconstructing the electrical structure of dust storms from locally observed electric field dataZhang, Huan; Zhou, You-He
doi: 10.1038/s41467-020-18759-0pmid: 33033243
While the electrification of dust storms is known to substantially affect the lifting and transport of dust particles, the electrical structure of dust storms and its underlying charge separation mechanisms are largely unclear. Here we present an inversion method, which is based on the Tikhonov regularization for inverting the electric field data collected in a near-ground observation array, to reconstruct the space-charge density and electric field in dust storms. After verifying the stability, robustness, and accuracy of the inversion procedure, we find that the reconstructed space-charge density exhibits a universal three-dimensional mosaic pattern of oppositely charged regions, probably due to the charge separation by turbulence. Furthermore, there are significant linear relationships between the reconstructed space-charge densities and measured PM10 dust concentrations at each measurement point, suggesting a multi-point large-scale charge equilibrium phenomenon in dust storms. These findings refine our understanding of charge separation mechanisms and particle transport in dust storms.